MLOps Engineer – Data Infrastructure

This is a fully remote position, open to applicants in France.

📋 Description

• Develop and manage infrastructure that supports the company's data and Machine Learning ecosystem.

• Provision and oversee both bare-metal and virtual machines, along with the environments they host.

• Design, configure, and maintain servers, virtual machines, and environments that facilitate data pipelines and ML development.

• Construct and uphold infrastructure and automation for data collection, preparation, validation, versioning, and accessibility.

• Automate the data flow from collection through preparation to downstream processes, including training triggers.

• Configure and manage Linux-based environments and servers utilized by ML and data teams.

• Deploy, configure, and sustain ML/data management platforms such as MLflow, DVC, ClearML, or their equivalents.

• Create reproducible offline environments featuring local package mirrors, private container registries, and offline artifact management.

• Implement dataset and artifact versioning to support experiment traceability.

• Operate the orchestration layer as a deployed service, covering installation, configuration, resource management, upgrades, and log/metric tracking.

• Assist in managing extensive and diverse datasets, including images, videos, structured data, and temporal/time-series data.

• Integrate data pipelines with the existing training and inference setups alongside the Data Engineer.

• Containerize data applications using Docker and incorporate them into deployment tools.

• Develop automation for provisioning environments, testing, deployment, and monitoring.

• Define and implement infrastructure and service monitoring, which includes data-quality and pipeline-health assessments.

• Monitor infrastructure and ML workloads while troubleshooting performance, availability, and configuration challenges.

• Promote efficient utilization of compute and storage resources.

• Contribute to the architecture and ongoing enhancement of the internal ML platform.

• Document infrastructure, configurations, deployment methods, and operational procedures.

⛳️ Requirements

• Demonstrated professional experience in MLOps, DevOps, data infrastructure, or a related engineering discipline.

• Strong practical knowledge of Linux, with confidence in extensive command-line operations.

• Experience in configuring and managing servers, virtual machines, and technical infrastructure.

• Significant expertise with Docker and containerized environments.

• Experience in developing environments that function offline or under limited network conditions: including local registries, mirrors, or disconnected setups.

• Practical experience with ML lifecycle/data management tools such as MLflow, DVC, ClearML, or similar frameworks.

• Proficient in Python, particularly for scripting, automation, and integration tasks.

• Strong understanding of data pipelines and the considerations related to large and varied datasets.

• Good grasp of Machine Learning development and deployment workflows.

• Experience in implementing CI/CD or comparable automation for software, data, or ML workloads.

• Solid understanding of Git and software development processes.

• Strong troubleshooting abilities across software, infrastructure, and configuration challenges.

• Capability to independently design and implement technical solutions, rather than solely operating an existing platform.

• Proficient in English, both written and spoken.

🏝️ Benefits

• Opportunity to work in a dynamic and innovative environment.

• Access to professional development and training resources.

• Flexible working arrangements.

• Competitive salary and performance-based incentives.